Facial emotion recognition on a dataset using Convolutional Neural Network
BildiriAçık Erişim
Özet
Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.
Anahtar Kelimeler
RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/munzur/670Resmi atıflarda kullanılan kalıcı adresİndeks kayıtları
Kaydın indeks veri tabanlarındaki sayfası (aktarımda alınan kayıt numarasıyla). Erişim, indeksin kendi koşullarına bağlıdır.Atıflar
- Scopus
- 68
- OpenAlex
- 83
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Tümen, Vedat | |
| dc.contributor.author | Söylemez, Ömer Faruk | |
| dc.contributor.author | Ergen, Burhan | |
| dc.date.accessioned | 2026-10-28T14:53:31.422276541Z | |
| dc.date.available | 2026-10-28T14:53:31.422276541Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database. | |
| dc.identifier.articlenumber | 8090281 | |
| dc.identifier.doi | 10.1109/idap.2017.8090281 | |
| dc.identifier.isbn | 978-153861880-6 | |
| dc.identifier.scopus | 2-s2.0-85039912842 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/munzur/670 | |
| dc.language.iso | tr | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.conference | 2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 | |
| dc.relation.ispartof | IDAP 2017 - International Artificial Intelligence and Data Processing Symposium | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Convolution neural networks | |
| dc.subject | Deep learning | |
| dc.subject | Facial expression recognition | |
| dc.subject | Image classification | |
| dc.subject.other | Artificial intelligence | |
| dc.subject.other | Classification (of information) | |
| dc.subject.other | Computer vision | |
| dc.subject.other | Content based retrieval | |
| dc.subject.other | Convolution | |
| dc.subject.other | Data handling | |
| dc.subject.other | Deep learning | |
| dc.subject.other | Image classification | |
| dc.subject.other | Image enhancement | |
| dc.subject.other | Image retrieval | |
| dc.subject.other | Neural networks | |
| dc.subject.other | Computer vision applications | |
| dc.subject.other | Content based image retrieval | |
| dc.subject.other | Convolution neural network | |
| dc.subject.other | Convolutional neural network | |
| dc.subject.other | Facial emotions | |
| dc.subject.other | Facial expression recognition | |
| dc.subject.other | Face recognition | |
| dc.title | Facial emotion recognition on a dataset using Convolutional Neural Network | |
| dc.type | Conference Object | |
| rdl.citation.openalex | 83 | |
| rdl.citation.scopus | 68 | |
| rdl.conference.date | 16 September 2017 through 17 September 2017 | |
| rdl.conference.place | Malatya | |
| rdl.oa.status | green | |
| rdl.oa.url | https://hdl.handle.net/11508/45743 | |
| rdl.source.index | SCOPUS |
